The proposed generative model is a Long Short Term Memory (LSTM) GAN that consists of a recurrent generator and two recurrent critics. Such a recurrent design is critical for solving the task, since the output is a set of SMPL shapes with a variable size. Additonally, when generating a set of SMPL shapes that are conditioned on a given text, the generator needs to have a memory component in order to monitor for which words the SMPL shapes have already been generated, and which words still need to be tended to in order to avoid repetitions. The critic is designed to be recurrent in the same way as the generator. This is because when the critic scores a given set of SMPL parameters conditioned on text, first it should be able to handle a variable-length set. Additionally, several aspects need to be assessed simultaneously, these include the realism of the shape, its semantic consistency with the text and whether all parts of the text have a matching shape. That is, when a pair of a SMPL shape set and text embedding \((s, x)\)  is assessed, the critic needs a memory component in order to output a final score that takes into account all these criteria. Since for example, it is possible that most shapes match the input text but perhaps a shape is missing or a duplicate shape exists or a non-matching shape is generated. Therefore, the entire shape set needs to be seen by the critic in order to be evaluated as in a many-to-one recurrent LSTM. The input to every iteration of the critic is a concatenation of the word embeddings and the SMPL shape generated in the same iteration \(t\)  in the generator or a single SMPL real shape . The critics are illustrated in figure REF . We use the Wasserstein GAN (WGAN) variant [1]} which uses the earth mover's distance and makes the training more stable than the Vanilla GAN.
